Unsupervised learning of probabilistic subspaces for multi-spectral and multi-temporal image-based disaster mapping-Reference-Cited by-同舟云学术

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Unsupervised learning of probabilistic subspaces for multi-spectral and multi-temporal image-based disaster mapping

Published:2023-09-13 Issue:6 Volume:34 Page:
ISSN:0932-8092
Container-title:Machine Vision and Applications
language:en
Short-container-title:Machine Vision and Applications

Author:

Okorie Azubuike,Kambhamettu Chandra,Makrogiannnis Sokratis^ORCID

Funder

National Institute of General Medical Sciences

National Institute on Minority Health and Health Disparities

Army Research Office

Publisher

Springer Science and Business Media LLC

Subject

Computer Science Applications,Computer Vision and Pattern Recognition,Hardware and Architecture,Software

Link

https://link.springer.com/content/pdf/10.1007/s00138-023-01451-w.pdf

Reference40 articles.

1. Voigt, S., Kemper, T., Riedlinger, T., Kiefl, R., Scholte, K., Mehl, H.: Satellite image analysis for disaster and crisis-management support. IEEE Trans. Geosci. Remote Sens. 45, 1520–1528 (2007). https://doi.org/10.1109/TGRS.2007.895830

2. Mori, N., Takahashi, T., Yasuda, T., Yanagisawa, H.: Survey of 2011 Tohoku earthquake tsunami inundation and run-up. Geophys. Res. Lett. 38(7) (2011)

3. Joyce, K.E., Belliss, S.E., Samsonov, S.V., McNeill, S.J., Glassey, P.J.: A review of the status of satellite remote sensing and image processing techniques for mapping natural hazards and disasters. Prog. Phys. Geogr. 33(2), 183–207 (2009)

4. Singh, A.: Review article digital change detection techniques using remotely-sensed data. Int. J. Remote Sens. 10(6), 989–1003 (1989)

5. Chen, J., Yuan, Z., Peng, J., Chen, L., Huang, H., Zhu, J., Liu, Y., Li, H.: Dasnet: dual attentive fully convolutional siamese networks for change detection in high-resolution satellite images. IEEE J. Sel. Topics Appl. Earth Obs. Remote Sens. 14, 1194–1206 (2020)

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